• Jing Liao, Kouichi Misaki, Tekehiro Uno, Kazuya Futami, Mitsutoshi Nakada, and Jiro Sakamoto.
• Division of Transdisciplinary Sciences, Graduate School of Frontier Science Initiative, Kanazawa University, Kanazawa, Ishikawa, Japan.
• World Neurosurg. 2024 Apr 1; 184: e166e177e166-e177.

BackgroundRecanalization poses challenges after coil embolization in cerebral aneurysms. Establishing predictive models for postembolization recanalization is important for clinical decision making. However, conventional statistical and machine learning (ML) models may overlook critical parameters during the initial selection process.MethodsIn this study, we automated the identification of significant hemodynamic parameters using a PointNet-based deep neural network (DNN), leveraging their three-dimensional spatial features. Further feature analysis was conducted using saliency mapping, an explainable artificial intelligence (XAI) technique. The study encompassed the analysis of velocity, pressure, and wall shear stress in both precoiling and postcoiling models derived from computational fluid dynamics simulations for 58 aneurysms.ResultsVelocity was identified as the most pivotal parameter, supported by the lowest P value from statistical analysis and the highest area under the receiver operating characteristic curves/precision-recall curves values from the DNN model. Moreover, visual XAI analysis showed that robust injection flow zones, with notable impingement points in precoiling models, as well as pronounced interplay between flow dynamics and the coiling plane, were important three-dimensional features in identifying the recanalized aneurysms.ConclusionsThe combination of DNN and XAI was found to be an accurate and explainable approach not only at predicting postembolization recanalization but also at discovering unknown features in the future.Copyright © 2024 Elsevier Inc. All rights reserved.

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